Towards Urban Resource Flow Estimates in Data Scarce Environments: The Case of African Cities The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Currie, Paul, et al. "Towards Urban Resource Flow Estimates in Data Scarce Environments: The Case of African Cities." Journal of Environmental Protection 6, 9 (September 2015): 1066-1083 © 2015 Author(s) As Published 10.4236/JEP.2015.69094 Publisher Scientific Research Publishing, Inc, Version Final published version Citable link https://hdl.handle.net/1721.1/124946 Terms of Use Creative Commons Attribution 4.0 International license Detailed Terms https://creativecommons.org/licenses/by/4.0/ Journal of Environmental Protection, 2015, 6, 1066-1083 Published Online September 2015 in SciRes. http://www.scirp.org/journal/jep http://dx.doi.org/10.4236/jep.2015.69094 Towards Urban Resource Flow Estimates in Data Scarce Environments: The Case of African Cities Paul Currie1*, Ethan Lay-Sleeper2, John E. Fernández2, Jenny Kim2, Josephine Kaviti Musango3 1School of Public Leadership, Stellenbosch University, Stellenbosch, South Africa 2Department of Architecture, Massachusetts Institute of Technology, Cambridge, USA 3School of Public Leadership, and the Centre for Renewable and Sustainable Energy Studies (CRSES), Stellenbosch, South Africa Email: *[email protected] Received 29 July 2015; accepted 20 September 2015; published 23 September 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Data sourcing challenges in African nations have led many African urban infrastructure develop- ments to be implemented with minimal scientific backing to support their success. In some cases this may directly impact a city’s ability to reach service delivery, economic growth and human de- velopment goals, let alone the city’s ability to protect ecosystem services upon which it relies. As an attempt to fill this gap, this paper describes an exploratory process used to determine city-level demographic, economic and resource flow data for African nations. The approach makes use of scaling and clustering techniques to form acceptable and utilizable representations of selected African cities. Variables that may serve as the strongest predictors for resource consumption in- tensity in African nations and cities were explored, in particular, the aspects of the Koppen Cli- mate Zones, estimates of average urban income and GDP, and the influence of urban primacy. It is expected that the approach examined will provide a step towards estimating and understanding African cities and their resource profiles. Keywords Urban Metabolism, Resource Flows, African Cities, Data Scaling, City Clustering 1. Introduction Humans have become an urban species. As of 2008, more than half of the human population occupies urban *Corresponding author. How to cite this paper: Currie, P., Lay-Sleeper, E., Fernández, J.E., Kim, J. and Musango, J.K. (2015) Towards Urban Re- source Flow Estimates in Data Scarce Environments: The Case of African Cities. Journal of Environmental Protection, 6, 1066-1083. http://dx.doi.org/10.4236/jep.2015.69094 P. Currie et al. hives of activity [1] [2]. This has happened through two major urbanization waves. The first occurred between 1750 and 1950, in which the urban population increased from 15 million to 423 million [3]. The continents af- fected by the first wave were mainly Europe and North America. The second urbanization wave is occurring now and expects that an additional 2.4 billion people will be living in African and Asian cities by 2050 [4]. Cities are concentrators of people and economic production, and therefore require large inputs of resources to fuel their development and growth. Common consensus suggests that cities currently produce roughly 80% of the global GDP and consume approximately 75% of global energy and materials. The concentration of resources forms large quantities of pollutants and wastes which, despite having originated in other global regions are typi- cally exported into the local environment, threatening natural systems as well as the ecosystem services upon which the city relies [5]. Satterthwaite [6] argues that while cities may be blamed for about 80% of global car- bon emissions, only about 35% are emitted within city boundaries. Rising consumption levels, not population growth, are identified as the real driver of climate change, and with that, understanding the differing consump- tion levels within cities is the key to understanding urbanization’s role in climate change. Satterthwaite [6] re- marks that cities may offer great opportunities to decouple greenhouse gas emissions from high-quality lifestyles, particularly in low- or middle-income countries. Ozbekhan’s [7] problematique and Morin and Kern’s [8] poly- crisis are apt terms to describe the emergent issues of overpopulation, pollution, ecosystem degradation, biodi- versity loss, scarcity of materials, social inequality, climate change and a loss of human connection to the natural world. As concentrators of the global polycrisis, cities are where the desire for a sustainable existence should also be fostered. The polycrisis is directly linked to resource flows: the sourcing, conversion, use and discharge of resources in various forms. These flows of resources are intimately related to the social makeup and processes of cities [9], the totality of which has been referred to as the urbanmetabolism. One definition of urban metabolism is the “sum total of the technical and socioeconomic processes that occur in cities, resulting in growth, production of energy, and elimination of waste” [10]. The concept of urban metabolism is recognized as useful for developing sustainable cities and urban areas [11] [12]. Indeed, analysis of the resource flow mechanisms in cities is neces- sary for understanding how cities may develop in the future and how they can support a growing population. Practical application of the concept of urban metabolism within urban systems requires quantification of the input flows and output flows in cities. These measures are sought to understand how urban systems function, the implication of these flows on the city’s hinterland and the biosphere, and their interactions with the operations of the social sphere [13]. In addition, studies of urban metabolism fall within the scope of sustainable development, and may involve constructing indicators, identifying special targets for sustainability, and developing decision support tools towards dematerialization or decarbonization [14]. To achieve an understanding and assessment of the nature and intensity of urban metabolism, there are a number of methods that have been utilized at city-level, including among others, 1) material flow analysis [15] [16]; 2) ecological footprint analysis [17] [18]; and 3) system dynamics modeling [19]. The primary methodological approach for establishing the urban metabolism of a city is material flow analy- sis (MFA) which makes use of a standardized framework for data collection, processing, and resource flow cal- culation developed by Eurostat [20]. However, the framework was formulated on the basis of national data availability and requirements of European member states. Recent studies of urban metabolism acknowledge the need for developing a comprehensive and standardized framework for metabolic flow analysis in the urban con- text, which not only includes increased data collection but also some degree of consensus on parameters that should be part of basic level reporting [21]. While this argument is put forward, major data challenges and limi- tations exist in most regions of the world and in relation to most cities. The situation is the same for cities on the African continent which include: 1) unavailability of data at city-level; 2) inconsistent formats of available data, reducing the ability for automatic use of conventional methods; 3) data confidentiality, making it difficult to access relevant data; 4) quality of data, particularly when information is not from credible sources; and 5) in- formality, in which significant proportions of resource flows occupy an undocumented informal sector. Some studies are developing approaches to deal with data challenges elsewhere. For instance Baynes and Bai [22] utilized a downscaling approach to reconstruct energy data for Melbourne city. Niza et al. [16] devised a method that could be utilized to extrapolate data from a country or region based on number of inhabitants, commuters, workers, sales, or produced waste. While these are useful city-level contributions to the field of ur- ban metabolism, the limitation is that detailed data and information are required to undertake these analyses. The implication is that, only cities with the most available data can be investigated. It would thus be challenging to 1067 P. Currie et al. apply these methodologies to the large number of cities in which data are limited, including most African cities. Questions have also been voiced about the most appropriate way to compare cities, particularly around the li- mitations of using a per-capita baseline. Differences in population distribution, social make-up, or city function are not fully recognized by a per capita measure, allowing quite different cities to show similar traits. Other me- chanisms for comparing cities, such as with a per-unit-GDP